# Driving Style Encoder: Situational Reward Adaptation for General-Purpose Planning in Automated Driving

arXiv: https://arxiv.org/abs/1912.03509 · HTML: https://mlsanity.com/paper/1912.03509/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Generating and Evolving Reward Functions for Highway Driving with Large Language Models](https://mlsanity.com/paper/2406.10540/) (2024)
- [Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning](https://mlsanity.com/paper/2007.05798/) (2020)
- [PP-TIL: Personalized Planning for Autonomous Driving with Instance-based Transfer Imitation Learning](https://mlsanity.com/paper/2407.18569/) (2024)
- [Pixel State Value Network for Combined Prediction and Planning in Interactive Environments](https://mlsanity.com/paper/2310.07706/) (2023)

## Abstract (arXiv)

General-purpose planning algorithms for automated driving combine mission, behavior, and local motion planning. Such planning algorithms map features of the environment and driving kinematics into complex reward functions. To achieve this, planning experts often rely on linear reward functions. The specification and tuning of these reward functions is a tedious process and requires significant experience. Moreover, a manually designed linear reward function does not generalize across different driving situations. In this work, we propose a deep learning approach based on inverse reinforcement learning that generates situation-dependent reward functions. Our neural network provides a mapping between features and actions of sampled driving policies of a model-predictive control-based planner and predicts reward functions for upcoming planning cycles. In our evaluation, we compare the driving style of reward functions predicted by our deep network against clustered and linear reward functions. Our proposed deep learning approach outperforms clustered linear reward functions and is at par with linear reward functions with a-priori knowledge about the situation.